These are the first experiments where I use version 2 chemistry. Read 2 is now the barcode and UMI Read 1 is the cDNA. This was done to avoid reading through the TSO on Ilumina read 1
Test version 2 protocol with a biological appliation from Zac Moore of Brain Cancer lab https://au-mynotebook.labarchives.com/share/Piper_Research_Project/MzEuMjAwMDAwMDAwMDAwMDAzfDE3MTc2NS8yNC9UcmVlTm9kZS8zMzczNDM0NjE1fDc5LjE5OTk5OTk5OTk5OTk5
Orientation is correct when looking at library size and mouse counts.
p1 <- platetools::raw_map(data=tb$Cell_Line,
well=tb$Well,
plate=96) + scale_fill_Publication()
p2 <- platetools::raw_map(data=log(tb$sum+1),
well=tb$Well,
plate=96) +
ggtitle("Library Size")
p3 <- platetools::raw_map(data=tb$Day_Exposure,
well=tb$Well,
plate=96) +
ggtitle("Days exposure to drug")
p4 <- platetools::raw_map(data=tb$Storage,
well=tb$Well,
plate=96) +
ggtitle("Storage conditions") + scale_fill_Publication()
p1 + p2 + p3 + p4tb_fact <- tb
tb_fact$Day_Exposure <- as.factor(tb$Day_Exposure)
tb_fact$Dose_M <- as.factor(tb$Dose_M)
p1 <- platetools::raw_map(data=tb_fact$Species,
well=tb$Well,
plate=96) +
scale_fill_discrete("Species")
p2 <- platetools::raw_map(data=tb_fact$Day_Exposure,
well=tb$Well,
plate=96) +
scale_fill_discrete("Timepoint (days)")
p3 <- platetools::raw_map(data=tb_fact$Dose_M,
well=tb$Well,
plate=96) +
guides(fill=guide_legend(ncol=2)) +
scale_fill_discrete("Dose (M)")
p4 <- platetools::raw_map(data=tb_fact$Drug,
well=tb$Well,
plate=96) +
scale_fill_discrete("Drug")
p1 + p4 + p3 + p2The metrics go as one would expect with dose and time treated
tb$Day_Exposure <- as_factor(tb$Day_Exposure)
plt1 <- ggplot(tb,
aes(x = Dose_M, y= sum, colour=Day_Exposure)) +
geom_point() +
ylab("Library Size (UMIs)") +
xlab("TMZ (M)") +
scale_y_continuous(trans='log10') + scale_x_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "bl") +
scale_colour_brewer(palette = "Dark2")
plt1Evidence that storage at 4C is insufficient to freeze the biological effect of temozolomide on the library size. In general metrics are poorer for storage at 4C.
storage <- tb %>%
filter(Day_Exposure == 7) %>%
filter(Dose_M != 0.0000001) %>%
filter(Dose_M != 0.000001) %>%
filter(Dose_M != 0.00000316) %>%
filter(Dose_M != 0.000000316) %>%
filter(Dose_M != 0.0000316)
storage %>%
dplyr::count(Dose_M,Storage)plt1 <- ggplot(storage,
aes(x = Storage, y= sum, colour=Drug)) +
geom_jitter(width = 0.1) +
ylab("Library Size (UMIs)") +
xlab("Storage") +
scale_y_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "l") +
scale_colour_brewer(palette = "Dark2")
plt1plt2 <- ggplot(storage,
aes(x = Storage, y= detected, colour=Drug)) +
geom_jitter(width = 0.1) +
ylab("Genes detected") +
xlab("Storage") +
scale_colour_brewer(palette = "Dark2")
plt2Further evidence that 4C storage does not inactivate Temozolomide effects.
plt3 <- ggplot(storage,
aes(x = Storage, y= subsets_Mito_percent, colour=Drug)) +
geom_jitter(width = 0.1) +
ylab("Mitochondrial %") + ylim(0,15) +
xlab("Storage") +
scale_colour_brewer(palette = "Dark2")
plt3## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRblas.so
## LAPACK: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scales_1.3.0 ggthemes_5.1.0
## [3] here_1.0.1 patchwork_1.2.0
## [5] platetools_0.1.7 scater_1.32.0
## [7] scuttle_1.14.0 lubridate_1.9.3
## [9] forcats_1.0.0 stringr_1.5.1
## [11] dplyr_1.1.4 purrr_1.0.2
## [13] readr_2.1.5 tidyr_1.3.1
## [15] tibble_3.2.1 ggplot2_3.5.1
## [17] tidyverse_2.0.0 SingleCellExperiment_1.26.0
## [19] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [21] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
## [23] IRanges_2.38.0 S4Vectors_0.42.0
## [25] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [27] matrixStats_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2
## [3] farver_2.1.2 vipor_0.4.7
## [5] viridis_0.6.5 fastmap_1.2.0
## [7] digest_0.6.35 rsvd_1.0.5
## [9] timechange_0.3.0 lifecycle_1.0.4
## [11] magrittr_2.0.3 compiler_4.4.0
## [13] rlang_1.1.3 sass_0.4.9
## [15] tools_4.4.0 utf8_1.2.4
## [17] yaml_2.3.8 knitr_1.46
## [19] labeling_0.4.3 S4Arrays_1.4.0
## [21] DelayedArray_0.30.1 RColorBrewer_1.1-3
## [23] abind_1.4-5 BiocParallel_1.38.0
## [25] withr_3.0.0 fansi_1.0.6
## [27] beachmat_2.20.0 colorspace_2.1-0
## [29] cli_3.6.2 rmarkdown_2.26
## [31] crayon_1.5.2 generics_0.1.3
## [33] rstudioapi_0.16.0 httr_1.4.7
## [35] tzdb_0.4.0 DelayedMatrixStats_1.26.0
## [37] ggbeeswarm_0.7.2 cachem_1.1.0
## [39] zlibbioc_1.50.0 parallel_4.4.0
## [41] XVector_0.44.0 vctrs_0.6.5
## [43] Matrix_1.7-0 jsonlite_1.8.8
## [45] BiocSingular_1.20.0 hms_1.1.3
## [47] BiocNeighbors_1.22.0 ggrepel_0.9.5
## [49] irlba_2.3.5.1 beeswarm_0.4.0
## [51] jquerylib_0.1.4 glue_1.7.0
## [53] codetools_0.2-20 stringi_1.8.4
## [55] gtable_0.3.5 UCSC.utils_1.0.0
## [57] ScaledMatrix_1.12.0 munsell_0.5.1
## [59] pillar_1.9.0 htmltools_0.5.8.1
## [61] GenomeInfoDbData_1.2.12 R6_2.5.1
## [63] sparseMatrixStats_1.16.0 rprojroot_2.0.4
## [65] evaluate_0.23 lattice_0.22-6
## [67] highr_0.10 bslib_0.7.0
## [69] Rcpp_1.0.12 gridExtra_2.3
## [71] SparseArray_1.4.3 xfun_0.44
## [73] pkgconfig_2.0.3